Chapter 7 Functional differences
7.1 Data preparation
# Aggregate bundle-level GIFTs into the compound level
GIFTs_elements <- to.elements(genome_gifts, GIFT_db2)
GIFTs_elements_filtered <- GIFTs_elements[rownames(GIFTs_elements) %in% genome_counts$genome, ]
GIFTs_elements_filtered <- as.data.frame(GIFTs_elements_filtered) %>%
select_if(~ !is.numeric(.) || sum(.) != 0)
elements <- GIFTs_elements_filtered %>%
as.data.frame()
# Aggregate element-level GIFTs into the function level
GIFTs_functions <- to.functions(GIFTs_elements_filtered, GIFT_db2)
functions <- GIFTs_functions %>%
as.data.frame()
# Aggregate function-level GIFTs into overall Biosynthesis, Degradation and Structural GIFTs
GIFTs_domains <- to.domains(GIFTs_functions, GIFT_db2)
domains <- GIFTs_domains %>%
as.data.frame()
# Get community-weighed average GIFTs per sample
GIFTs_elements_community <- to.community(GIFTs_elements_filtered, genome_counts_filt %>% column_to_rownames(., "genome") %>% tss(), GIFT_db2)
GIFTs_functions_community <- to.community(GIFTs_functions, genome_counts_filt %>% column_to_rownames(., "genome") %>% tss(), GIFT_db2)
GIFTs_domains_community <- to.community(GIFTs_domains, genome_counts_filt %>% column_to_rownames(., "genome") %>% tss(), GIFT_db2)
uniqueGIFT_db<- unique(GIFT_db2[c(2,4,5,6)]) %>% unite("Function",Function:Element, sep= "_", remove=FALSE)7.2 Genomes GIFT profiles
GIFTs_elements %>%
as_tibble(., rownames = "MAG") %>%
reshape2::melt() %>%
rename(Code_element = variable, GIFT = value) %>%
inner_join(GIFT_db2,by="Code_element") %>%
ggplot(., aes(x=Code_element, y=MAG, fill=GIFT, group=Code_function))+
geom_tile()+
scale_y_discrete(guide = guide_axis(check.overlap = TRUE))+
scale_x_discrete(guide = guide_axis(check.overlap = TRUE))+
scale_fill_gradientn(colours=rev(c("#d53e4f", "#f46d43", "#fdae61", "#fee08b", "#e6f598", "#abdda4", "#ddf1da")))+
facet_grid(. ~ Code_function, scales = "free", space = "free")+
theme_grey(base_size=8)+
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1),strip.text.x = element_text(angle = 90))
7.3 Function level
GIFTs_functions_community %>%
as.data.frame() %>%
rownames_to_column(var="sample") %>%
filter(sample!="AD69") %>%
pivot_longer(!sample,names_to="trait",values_to="gift") %>%
left_join(sample_metadata, by = join_by(sample == Tube_code)) %>%
ggplot(aes(x=trait,y=time_point,fill=gift)) +
geom_tile(colour="white", size=0.2)+
scale_fill_gradientn(colours=rev(c("#d53e4f", "#f46d43", "#fdae61", "#fee08b", "#e6f598", "#abdda4", "#ddf1da")))+
facet_grid(type ~ ., scales="free",space="free")
7.4 Element level
GIFTs_elements_community_merged<-GIFTs_elements_community %>%
as.data.frame() %>%
rownames_to_column(var="sample") %>%
filter(sample!="AD69") %>%
pivot_longer(!sample,names_to="trait",values_to="gift") %>%
left_join(sample_metadata, by = join_by(sample == Tube_code))%>%
mutate(functionid = substr(trait, 1, 3)) %>%
mutate(trait = case_when(
trait %in% GIFT_db2$Code_element ~ GIFT_db2$Element[match(trait, GIFT_db2$Code_element)],
TRUE ~ trait
)) %>%
mutate(functionid = case_when(
functionid %in% GIFT_db2$Code_function ~ GIFT_db2$Function[match(functionid, GIFT_db2$Code_function)],
TRUE ~ functionid
)) %>%
mutate(trait=factor(trait,levels=unique(GIFT_db2$Element))) %>%
mutate(functionid=factor(functionid,levels=unique(GIFT_db2$Function)))
# Create an interaction variable for time_point and sample
GIFTs_elements_community_merged$interaction_var <- interaction(GIFTs_elements_community_merged$sample, GIFTs_elements_community_merged$time_point)
ggplot(GIFTs_elements_community_merged,aes(x=interaction_var,y=trait,fill=gift)) +
geom_tile(colour="white", linewidth=0.2)+
scale_fill_gradientn(colours=rev(c("#d53e4f", "#f46d43", "#fdae61", "#fee08b", "#e6f598", "#abdda4", "#ddf1da")))+
facet_grid(functionid ~ type, scales="free",space="free") +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1, size=5),
strip.text.y = element_text(angle = 0)) +
labs(y="Traits",x="Time_point",fill="GIFT")+
scale_x_discrete(labels = function(x) gsub(".*\\.", "", x))
7.5 Comparison of samples from the 0 Time_point (0_Wild)
7.5.1 GIFTs Functional community
GIFTs_functions_community %>%
rowMeans() %>%
as_tibble(., rownames = "sample") %>%
left_join(sample_metadata, by = join_by(sample == Tube_code)) %>%
filter(time_point=="0_Wild") %>%
group_by(Population) %>%
summarise(MCI = mean(value), sd = sd(value))# A tibble: 2 × 3
Population MCI sd
<chr> <dbl> <dbl>
1 Cold_wet 0.346 0.0194
2 Hot_dry 0.327 0.0244MCI_func_wild <- GIFTs_functions_community %>%
rowMeans() %>%
as_tibble(., rownames = "sample") %>%
left_join(sample_metadata, by = join_by(sample == Tube_code)) %>%
filter(time_point=="0_Wild")
shapiro.test(MCI_func_wild$value) #normality test
Shapiro-Wilk normality test
data: MCI_func_wild$value
W = 0.96385, p-value = 0.4503 Df Sum Sq Mean Sq F value Pr(>F)
Population 1 0.002244 0.0022440 5.025 0.0341 *
Residuals 25 0.011165 0.0004466
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 17.5.1.1 GIFT test visualisation
GIFTs_functions_community %>%
as.data.frame() %>%
rownames_to_column("sample") %>%
left_join(sample_metadata, by = join_by(sample == Tube_code)) %>%
filter(time_point=="0_Wild") %>%
select(c(1:21, 24)) %>%
pivot_longer(-c(sample,Population),names_to = "trait", values_to = "value") %>%
mutate(trait = case_when(
trait %in% GIFT_db2$Code_function ~ GIFT_db2$Function[match(trait, GIFT_db2$Code_function)],
TRUE ~ trait
)) %>%
mutate(trait=factor(trait,levels=unique(GIFT_db2$Function))) %>%
ggplot(aes(x=value, y=Population, group=Population, fill=Population, color=Population)) +
geom_boxplot() +
scale_color_manual(name="Population",
breaks=c("Cold_wet","Hot_dry"),
labels=c("Cold","Hot"),
values=c('#008080', "#d57d2c")) +
scale_fill_manual(name="Population",
breaks=c("Cold_wet","Hot_dry"),
labels=c("Cold","Hot"),
values=c('#00808050', "#d57d2c50")) +
facet_grid(trait ~ ., space="free", scales="free") +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1),
strip.text.y = element_text(angle = 0)) +
labs(y="Traits",x="Metabolic capacity index")
7.5.2 GIFTs Domain community
GIFTs_domains_community %>%
rowMeans() %>%
as_tibble(., rownames = "sample") %>%
left_join(sample_metadata, by = join_by(sample == Tube_code)) %>%
filter(time_point=="0_Wild") %>%
group_by(species) %>%
summarise(MCI = mean(value), sd = sd(value))# A tibble: 2 × 3
species MCI sd
<chr> <dbl> <dbl>
1 Podarcis_liolepis 0.337 0.0200
2 Podarcis_muralis 0.350 0.0232MCI_domain_wild <- GIFTs_domains_community %>%
rowMeans() %>%
as_tibble(., rownames = "sample") %>%
left_join(sample_metadata, by = join_by(sample == Tube_code)) %>%
filter(time_point=="0_Wild")
shapiro.test(MCI_domain_wild$value) #normality test
Shapiro-Wilk normality test
data: MCI_domain_wild$value
W = 0.97775, p-value = 0.8085 Df Sum Sq Mean Sq F value Pr(>F)
Population 1 0.000977 0.0009768 1.98 0.172
Residuals 25 0.012336 0.0004935 7.5.3 GIFTs Elements community
GIFTs_elements_community %>%
rowMeans() %>%
as_tibble(., rownames = "sample") %>%
left_join(sample_metadata, by = join_by(sample == Tube_code)) %>%
filter(time_point=="0_Wild") %>%
group_by(species) %>%
summarise(MCI = mean(value), sd = sd(value))# A tibble: 2 × 3
species MCI sd
<chr> <dbl> <dbl>
1 Podarcis_liolepis 0.313 0.0329
2 Podarcis_muralis 0.345 0.0233MCI_element_wild <- GIFTs_elements_community %>%
rowMeans() %>%
as_tibble(., rownames = "sample") %>%
left_join(sample_metadata, by = join_by(sample == Tube_code)) %>%
filter(time_point=="0_Wild")
shapiro.test(MCI_element_wild$value) #normality test
Shapiro-Wilk normality test
data: MCI_element_wild$value
W = 0.96004, p-value = 0.3703 Df Sum Sq Mean Sq F value Pr(>F)
Population 1 0.006235 0.006235 8.709 0.00679 **
Residuals 25 0.017898 0.000716
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 17.6 Comparison of samples from the 1st Time_point (1_Acclimation)
7.6.1 GIFTs Functional community
GIFTs_functions_community %>%
rowMeans() %>%
as_tibble(., rownames = "sample") %>%
left_join(sample_metadata, by = join_by(sample == Tube_code)) %>%
filter(time_point=="1_Acclimation") %>%
group_by(species) %>%
summarise(MCI = mean(value), sd = sd(value))# A tibble: 2 × 3
species MCI sd
<chr> <dbl> <dbl>
1 Podarcis_liolepis 0.348 0.0158
2 Podarcis_muralis 0.329 0.0319MCI_func_accli <- GIFTs_functions_community %>%
rowMeans() %>%
as_tibble(., rownames = "sample") %>%
left_join(sample_metadata, by = join_by(sample == Tube_code)) %>%
filter(time_point=="1_Acclimation")
shapiro.test(MCI_func_accli$value) #normality test
Shapiro-Wilk normality test
data: MCI_func_accli$value
W = 0.92832, p-value = 0.0707 Df Sum Sq Mean Sq F value Pr(>F)
Population 1 0.002028 0.0020275 2.667 0.116
Residuals 24 0.018247 0.0007603 7.6.1.1 GIFT test visualisation
GIFTs_functions_community %>%
as.data.frame() %>%
rownames_to_column("sample") %>%
left_join(sample_metadata, by = join_by(sample == Tube_code)) %>%
filter(time_point=="1_Acclimation") %>%
select(c(1:21, 24)) %>%
pivot_longer(-c(sample,Population),names_to = "trait", values_to = "value") %>%
mutate(trait = case_when(
trait %in% GIFT_db2$Code_function ~ GIFT_db2$Function[match(trait, GIFT_db2$Code_function)],
TRUE ~ trait
)) %>%
mutate(trait=factor(trait,levels=unique(GIFT_db2$Function))) %>%
ggplot(aes(x=value, y=Population, group=Population, fill=Population, color=Population)) +
geom_boxplot() +
scale_color_manual(name="Population",
breaks=c("Cold_wet","Hot_dry"),
labels=c("Cold","Hot"),
values=c('#008080', "#d57d2c")) +
scale_fill_manual(name="Population",
breaks=c("Cold_wet","Hot_dry"),
labels=c("Cold","Hot"),
values=c('#00808050', "#d57d2c50")) +
facet_grid(trait ~ ., space="free", scales="free") +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1),
strip.text.y = element_text(angle = 0)) +
labs(y="Traits",x="Metabolic capacity index")
7.6.2 GIFTs Domain community
GIFTs_domains_community %>%
rowMeans() %>%
as_tibble(., rownames = "sample") %>%
left_join(sample_metadata, by = join_by(sample == Tube_code)) %>%
filter(time_point=="1_Acclimation") %>%
group_by(species) %>%
summarise(MCI = mean(value), sd = sd(value))# A tibble: 2 × 3
species MCI sd
<chr> <dbl> <dbl>
1 Podarcis_liolepis 0.344 0.0133
2 Podarcis_muralis 0.328 0.0371MCI_domain_accli <- GIFTs_domains_community %>%
rowMeans() %>%
as_tibble(., rownames = "sample") %>%
left_join(sample_metadata, by = join_by(sample == Tube_code)) %>%
filter(time_point=="1_Acclimation")
shapiro.test(MCI_domain_accli$value) #normality test
Shapiro-Wilk normality test
data: MCI_domain_accli$value
W = 0.95031, p-value = 0.2358 Df Sum Sq Mean Sq F value Pr(>F)
Population 1 0.001423 0.0014229 1.459 0.239
Residuals 24 0.023404 0.0009751 7.6.3 GIFTs Elements community
GIFTs_elements_community %>%
rowMeans() %>%
as_tibble(., rownames = "sample") %>%
left_join(sample_metadata, by = join_by(sample == Tube_code)) %>%
filter(time_point=="1_Acclimation") %>%
group_by(species) %>%
summarise(MCI = mean(value), sd = sd(value))# A tibble: 2 × 3
species MCI sd
<chr> <dbl> <dbl>
1 Podarcis_liolepis 0.350 0.0225
2 Podarcis_muralis 0.331 0.0319MCI_element_accli <- GIFTs_elements_community %>%
rowMeans() %>%
as_tibble(., rownames = "sample") %>%
left_join(sample_metadata, by = join_by(sample == Tube_code)) %>%
filter(time_point=="1_Acclimation")
shapiro.test(MCI_element_accli$value) #normality test
Shapiro-Wilk normality test
data: MCI_element_accli$value
W = 0.91889, p-value = 0.04235 Df Sum Sq Mean Sq F value Pr(>F)
Population 1 0.002272 0.0022722 2.685 0.114
Residuals 24 0.020309 0.0008462 7.7 Comparison of samples from the 5th Time_point (5_Post-FMT1)
7.7.1 GIFTs Functional community
GIFTs_functions_community %>%
rowMeans() %>%
as_tibble(., rownames = "sample") %>%
left_join(sample_metadata, by = join_by(sample == Tube_code)) %>%
filter(time_point=="5_Post-FMT1") %>%
group_by(type) %>%
summarise(MCI = mean(value), sd = sd(value))# A tibble: 3 × 3
type MCI sd
<chr> <dbl> <dbl>
1 Control 0.373 0.0247
2 Hot_control 0.372 0.0367
3 Treatment 0.353 0.0186MCI_tm5 <- GIFTs_functions_community %>%
rowMeans() %>%
as_tibble(., rownames = "sample") %>%
left_join(sample_metadata, by = join_by(sample == Tube_code)) %>%
filter(time_point=="5_Post-FMT1")
shapiro.test(MCI_tm5$value)#normality test
Shapiro-Wilk normality test
data: MCI_tm5$value
W = 0.92343, p-value = 0.05415 Df Sum Sq Mean Sq F value Pr(>F)
type 2 0.00216 0.0010798 1.371 0.274
Residuals 23 0.01811 0.0007875 7.7.1.1 GIFT test visualisation
GIFTs_functions_community %>%
as.data.frame() %>%
rownames_to_column("sample") %>%
left_join(sample_metadata, by = join_by(sample == Tube_code)) %>%
filter(time_point=="5_Post-FMT1") %>%
select(c(1:21, 27)) %>%
pivot_longer(-c(sample,type),names_to = "trait", values_to = "value") %>%
mutate(trait = case_when(
trait %in% GIFT_db2$Code_function ~ GIFT_db2$Function[match(trait, GIFT_db2$Code_function)],
TRUE ~ trait
)) %>%
mutate(trait=factor(trait,levels=unique(GIFT_db2$Function))) %>%
ggplot(aes(x=value, y=type, group=type, fill=type, color=type)) +
geom_boxplot() +
scale_color_manual(name="type",
breaks=c("Control","Hot_control", "Treatment"),
labels=c("Cold-Cold","Hot-Hot", "Cold-Hot"),
values=c("#4477AA","#d57d2c","#76b183")) +
scale_fill_manual(name="type",
breaks=c("Control","Hot_control", "Treatment"),
labels=c("Cold-Cold","Hot-Hot", "Cold-Hot"),
values=c("#4477AA50","#d57d2c50","#76b18350")) +
facet_grid(trait ~ ., space="free", scales="free") +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1),
strip.text.y = element_text(angle = 0)) +
labs(y="Traits",x="Metabolic capacity index")
7.7.2 GIFTs Domain community
GIFTs_domains_community %>%
rowMeans() %>%
as_tibble(., rownames = "sample") %>%
left_join(sample_metadata, by = join_by(sample == Tube_code)) %>%
filter(time_point=="5_Post-FMT1") %>%
group_by(type) %>%
summarise(MCI = mean(value), sd = sd(value))# A tibble: 3 × 3
type MCI sd
<chr> <dbl> <dbl>
1 Control 0.362 0.0223
2 Hot_control 0.362 0.0353
3 Treatment 0.341 0.0232MCI_tm5_domain <- GIFTs_domains_community %>%
rowMeans() %>%
as_tibble(., rownames = "sample") %>%
left_join(sample_metadata, by = join_by(sample == Tube_code)) %>%
filter(time_point=="5_Post-FMT1")
shapiro.test(MCI_tm5_domain$value)#normality test
Shapiro-Wilk normality test
data: MCI_tm5_domain$value
W = 0.91677, p-value = 0.03779 Df Sum Sq Mean Sq F value Pr(>F)
type 2 0.002492 0.0012461 1.615 0.221
Residuals 23 0.017746 0.0007715 7.7.3 GIFTs Elements community
GIFTs_elements_community %>%
rowMeans() %>%
as_tibble(., rownames = "sample") %>%
left_join(sample_metadata, by = join_by(sample == Tube_code)) %>%
filter(time_point=="5_Post-FMT1") %>%
group_by(type) %>%
summarise(MCI = mean(value), sd = sd(value))# A tibble: 3 × 3
type MCI sd
<chr> <dbl> <dbl>
1 Control 0.380 0.0280
2 Hot_control 0.379 0.0372
3 Treatment 0.359 0.0214MCI_tm5_ele<- GIFTs_elements_community %>%
rowMeans() %>%
as_tibble(., rownames = "sample") %>%
left_join(sample_metadata, by = join_by(sample == Tube_code)) %>%
filter(time_point=="5_Post-FMT1")
shapiro.test(MCI_tm5_ele$value)#normality test
Shapiro-Wilk normality test
data: MCI_tm5_ele$value
W = 0.97214, p-value = 0.6794 Df Sum Sq Mean Sq F value Pr(>F)
type 2 0.002312 0.0011561 1.297 0.293
Residuals 23 0.020506 0.0008916 7.8 Comparison of samples from the 6th Time_point (6_Post-FMT2)
7.8.1 GIFTs Functional community
GIFTs_functions_community %>%
rowMeans() %>%
as_tibble(., rownames = "sample") %>%
left_join(sample_metadata, by = join_by(sample == Tube_code)) %>%
filter(time_point=="6_Post-FMT2") %>%
group_by(type) %>%
summarise(MCI = mean(value), sd = sd(value))# A tibble: 3 × 3
type MCI sd
<chr> <dbl> <dbl>
1 Control 0.352 0.0223
2 Hot_control 0.350 0.0293
3 Treatment 0.346 0.0255MCI_tm6 <- GIFTs_functions_community %>%
rowMeans() %>%
as_tibble(., rownames = "sample") %>%
left_join(sample_metadata, by = join_by(sample == Tube_code)) %>%
filter(time_point=="6_Post-FMT2")
shapiro.test(MCI_tm6$value)#normality test
Shapiro-Wilk normality test
data: MCI_tm6$value
W = 0.96248, p-value = 0.4203 Df Sum Sq Mean Sq F value Pr(>F)
type 2 0.00017 0.0000852 0.127 0.881
Residuals 24 0.01604 0.0006682 7.8.1.1 GIFT test visualisation
GIFTs_functions_community %>%
as.data.frame() %>%
rownames_to_column("sample") %>%
left_join(sample_metadata, by = join_by(sample == Tube_code)) %>%
filter(time_point=="6_Post-FMT2") %>%
select(c(1:21, 27)) %>%
pivot_longer(-c(sample,type),names_to = "trait", values_to = "value") %>%
mutate(trait = case_when(
trait %in% GIFT_db2$Code_function ~ GIFT_db2$Function[match(trait, GIFT_db2$Code_function)],
TRUE ~ trait
)) %>%
mutate(trait=factor(trait,levels=unique(GIFT_db2$Function))) %>%
ggplot(aes(x=value, y=type, group=type, fill=type, color=type)) +
geom_boxplot() +
scale_color_manual(name="type",
breaks=c("Control","Hot_control", "Treatment"),
labels=c("Cold-Cold","Hot-Hot", "Cold-Hot"),
values=c("#4477AA","#d57d2c","#76b183")) +
scale_fill_manual(name="type",
breaks=c("Control","Hot_control", "Treatment"),
labels=c("Cold-Cold","Hot-Hot", "Cold-Hot"),
values=c("#4477AA50","#d57d2c50","#76b18350")) +
facet_grid(trait ~ ., space="free", scales="free") +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1),
strip.text.y = element_text(angle = 0)) +
labs(y="Traits",x="Metabolic capacity index")
7.8.2 GIFTs Domain community
GIFTs_domains_community %>%
rowMeans() %>%
as_tibble(., rownames = "sample") %>%
left_join(sample_metadata, by = join_by(sample == Tube_code)) %>%
filter(time_point=="6_Post-FMT2") %>%
group_by(type) %>%
summarise(MCI = mean(value), sd = sd(value))# A tibble: 3 × 3
type MCI sd
<chr> <dbl> <dbl>
1 Control 0.348 0.0279
2 Hot_control 0.352 0.0322
3 Treatment 0.340 0.0243MCI_domains_tm6 <- GIFTs_domains_community %>%
rowMeans() %>%
as_tibble(., rownames = "sample") %>%
left_join(sample_metadata, by = join_by(sample == Tube_code)) %>%
filter(time_point=="6_Post-FMT2")
shapiro.test(MCI_domains_tm6$value)#normality test
Shapiro-Wilk normality test
data: MCI_domains_tm6$value
W = 0.94369, p-value = 0.1502 Df Sum Sq Mean Sq F value Pr(>F)
type 2 0.00074 0.0003699 0.461 0.636
Residuals 24 0.01925 0.0008021 7.8.3 GIFTs Elements community
GIFTs_elements_community %>%
rowMeans() %>%
as_tibble(., rownames = "sample") %>%
left_join(sample_metadata, by = join_by(sample == Tube_code)) %>%
filter(time_point=="6_Post-FMT2") %>%
group_by(type) %>%
summarise(MCI = mean(value), sd = sd(value))# A tibble: 3 × 3
type MCI sd
<chr> <dbl> <dbl>
1 Control 0.357 0.0215
2 Hot_control 0.347 0.0302
3 Treatment 0.350 0.0293MCI_ele_tm6 <- GIFTs_elements_community %>%
rowMeans() %>%
as_tibble(., rownames = "sample") %>%
left_join(sample_metadata, by = join_by(sample == Tube_code)) %>%
filter(time_point=="6_Post-FMT2")
shapiro.test(MCI_ele_tm6$value) #normality test
Shapiro-Wilk normality test
data: MCI_ele_tm6$value
W = 0.95139, p-value = 0.2316 Df Sum Sq Mean Sq F value Pr(>F)
type 2 0.000488 0.0002438 0.328 0.724
Residuals 24 0.017851 0.0007438 7.9 Domain level
7.9.1 Comparison of samples from the 0 Time_point (0_Wild)
#Merge the functional domains with the metadata
merge_gift_wild<- GIFTs_domains_community %>%
as.data.frame() %>%
rownames_to_column(., "Tube_code") %>%
inner_join(., sample_metadata_wild, by="Tube_code")#Biosynthesis
p1 <-merge_gift_wild %>%
ggplot(aes(x=species,y=Biosynthesis,color=species,fill=species))+
geom_jitter(width = 0.2, size = 1.5, show.legend = FALSE)+
geom_boxplot(alpha=0.2,outlier.shape = NA, width = 0.5, show.legend = FALSE, coef=0)+
stat_compare_means() +
theme(axis.text.x = element_text(vjust = 0.5, size=10),
axis.text.y = element_text(size=10),
axis.title=element_text(size=12,face="bold"),
axis.text = element_text(face="bold", size=18),
panel.background = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(size = 0.5, linetype = "solid", colour = "black"),
legend.text = element_text(size=10),
legend.title = element_text(size=12),
legend.position="none",
legend.key.size = unit(1, 'cm'),
strip.text.x = element_text(size = 12, color = "black", face = "bold"))+
labs( x = "Species")
#Degradation
p2 <-merge_gift_wild %>%
ggplot(aes(x=species,y=Degradation,color=species,fill=species))+
geom_jitter(width = 0.2, size = 1.45, show.legend = FALSE)+
geom_boxplot(alpha=0.2,outlier.shape = NA, width = 0.5, show.legend = FALSE, coef=0)+
stat_compare_means() +
theme(axis.text.x = element_text(vjust = 0.5, size=10),
axis.text.y = element_text(size=10),
axis.title=element_text(size=12,face="bold"),
panel.background = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(size = 0.5, linetype = "solid", colour = "black"),
legend.text = element_text(size=10),
legend.title = element_text(size=12),
legend.position="none",
legend.key.size = unit(1, 'cm'),
strip.text.x = element_text(size = 12, color = "black", face = "bold"))+
labs( x = "Species")
#Structure
p3 <-merge_gift_wild %>%
ggplot(aes(x=species,y=Structure,color=species,fill=species))+
geom_jitter(width = 0.2, size = 1.5, show.legend = FALSE)+
geom_boxplot(alpha=0.2,outlier.shape = NA, width = 0.5, show.legend = FALSE, coef=0)+
stat_compare_means() +
theme(axis.text.x = element_text(vjust = 3, size=10),
axis.text.y = element_text(size=10),
axis.title=element_text(size=12,face="bold"),
axis.text = element_text(face="bold", size=18),
panel.background = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(size = 0.5, linetype = "solid", colour = "black"),
legend.text = element_text(size=10),
legend.title = element_text(size=12),
legend.position="none",
legend.key.size = unit(1, 'cm'),
strip.text.x = element_text(size = 12, color = "black", face = "bold"))+
labs( x = "Species")
7.9.2 Comparison of samples from the 1st Time_point (1_Acclimation)
#Merge the functional domains with the metadata
merge_gift_accli<- GIFTs_domains_community %>%
as.data.frame() %>%
rownames_to_column(., "Tube_code") %>%
inner_join(., sample_metadata_accli, by="Tube_code")#Biosynthesis
p1 <-merge_gift_accli %>%
ggplot(aes(x=Population,y=Biosynthesis,color=Population,fill=Population))+
geom_jitter(width = 0.2, size = 1.5, show.legend = FALSE)+
geom_boxplot(alpha=0.2,outlier.shape = NA, width = 0.5, show.legend = FALSE, coef=0)+
stat_compare_means() +
theme(axis.text.x = element_text(vjust = 0.5, size=10),
axis.text.y = element_text(size=10),
axis.title=element_text(size=12,face="bold"),
axis.text = element_text(face="bold", size=18),
panel.background = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(size = 0.5, linetype = "solid", colour = "black"),
legend.text = element_text(size=10),
legend.title = element_text(size=12),
legend.position="none",
legend.key.size = unit(1, 'cm'),
strip.text.x = element_text(size = 12, color = "black", face = "bold"))+
labs( x = "Population")
#Degradation
p2 <-merge_gift_accli %>%
ggplot(aes(x=Population,y=Degradation,color=Population,fill=Population))+
geom_jitter(width = 0.2, size = 1.45, show.legend = FALSE)+
geom_boxplot(alpha=0.2,outlier.shape = NA, width = 0.5, show.legend = FALSE, coef=0)+
stat_compare_means() +
theme(axis.text.x = element_text(vjust = 0.5, size=10),
axis.text.y = element_text(size=10),
axis.title=element_text(size=12,face="bold"),
panel.background = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(size = 0.5, linetype = "solid", colour = "black"),
legend.text = element_text(size=10),
legend.title = element_text(size=12),
legend.position="none",
legend.key.size = unit(1, 'cm'),
strip.text.x = element_text(size = 12, color = "black", face = "bold"))+
labs( x = "Population")
#Structure
p3 <-merge_gift_accli %>%
ggplot(aes(x=Population,y=Structure,color=Population,fill=Population))+
geom_jitter(width = 0.2, size = 1.5, show.legend = FALSE)+
geom_boxplot(alpha=0.2,outlier.shape = NA, width = 0.5, show.legend = FALSE, coef=0)+
stat_compare_means() +
theme(axis.text.x = element_text(vjust = 3, size=10),
axis.text.y = element_text(size=10),
axis.title=element_text(size=12,face="bold"),
axis.text = element_text(face="bold", size=18),
panel.background = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(size = 0.5, linetype = "solid", colour = "black"),
legend.text = element_text(size=10),
legend.title = element_text(size=12),
legend.position="none",
legend.key.size = unit(1, 'cm'),
strip.text.x = element_text(size = 12, color = "black", face = "bold"))+
labs( x = "Population")
7.9.3 Comparison of samples from the 5th Time_point (5_Post-FMT1)
#Merge the functional domains with the metadata
merge_gift_tm5<- GIFTs_domains_community %>%
as.data.frame() %>%
rownames_to_column(., "Tube_code") %>%
inner_join(., sample_metadata_tm5, by="Tube_code")#Biosynthesis
p1 <-merge_gift_tm5 %>%
ggplot(aes(x=type,y=Biosynthesis,color=type,fill=type))+
geom_jitter(width = 0.2, size = 1.5, show.legend = FALSE)+
geom_boxplot(alpha=0.2,outlier.shape = NA, width = 0.5, show.legend = FALSE, coef=0)+
stat_compare_means() +
theme(axis.text.x = element_text(vjust = 0.5, size=10),
axis.text.y = element_text(size=10),
axis.title=element_text(size=12,face="bold"),
axis.text = element_text(face="bold", size=18),
panel.background = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(size = 0.5, linetype = "solid", colour = "black"),
legend.text = element_text(size=10),
legend.title = element_text(size=12),
legend.position="none",
legend.key.size = unit(1, 'cm'),
strip.text.x = element_text(size = 12, color = "black", face = "bold"))+
labs( x = "Type")
#Degradation
p2 <-merge_gift_tm5 %>%
ggplot(aes(x=type,y=Degradation,color=type,fill=type))+
geom_jitter(width = 0.2, size = 1.45, show.legend = FALSE)+
geom_boxplot(alpha=0.2,outlier.shape = NA, width = 0.5, show.legend = FALSE, coef=0)+
stat_compare_means() +
theme(axis.text.x = element_text(vjust = 0.5, size=10),
axis.text.y = element_text(size=10),
axis.title=element_text(size=12,face="bold"),
panel.background = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(size = 0.5, linetype = "solid", colour = "black"),
legend.text = element_text(size=10),
legend.title = element_text(size=12),
legend.position="none",
legend.key.size = unit(1, 'cm'),
strip.text.x = element_text(size = 12, color = "black", face = "bold"))+
labs( x = "Type")
#Structure
p3 <-merge_gift_tm5 %>%
ggplot(aes(x=type,y=Structure,color=type,fill=type))+
geom_jitter(width = 0.2, size = 1.5, show.legend = FALSE)+
geom_boxplot(alpha=0.2,outlier.shape = NA, width = 0.5, show.legend = FALSE, coef=0)+
stat_compare_means() +
theme(axis.text.x = element_text(vjust = 3, size=10),
axis.text.y = element_text(size=10),
axis.title=element_text(size=12,face="bold"),
axis.text = element_text(face="bold", size=18),
panel.background = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(size = 0.5, linetype = "solid", colour = "black"),
legend.text = element_text(size=10),
legend.title = element_text(size=12),
legend.position="none",
legend.key.size = unit(1, 'cm'),
strip.text.x = element_text(size = 12, color = "black", face = "bold"))+
labs( x = "Type")
7.9.4 Comparison of samples from the 6th Time_point (6_Post-FMT2)
7.9.4.1 CI vs CC
#Merge the functional domains with the metadata
merge_gift_TM6 <- GIFTs_domains_community %>%
as.data.frame() %>%
rownames_to_column(., "Tube_code") %>%
inner_join(., sample_metadata_TM6, by="Tube_code")#Biosynthesis
p1 <-merge_gift_TM6 %>%
ggplot(aes(x=type,y=Biosynthesis,color=type,fill=type))+
geom_jitter(width = 0.2, size = 1.5, show.legend = FALSE)+
geom_boxplot(alpha=0.2,outlier.shape = NA, width = 0.5, show.legend = FALSE, coef=0)+
stat_compare_means() +
theme(axis.text.x = element_text(vjust = 0.5, size=10),
axis.text.y = element_text(size=10),
axis.title=element_text(size=12,face="bold"),
axis.text = element_text(face="bold", size=18),
panel.background = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(size = 0.5, linetype = "solid", colour = "black"),
legend.text = element_text(size=10),
legend.title = element_text(size=12),
legend.position="none",
legend.key.size = unit(1, 'cm'),
strip.text.x = element_text(size = 12, color = "black", face = "bold"))+
labs( x = "Type")
#Degradation
p2 <-merge_gift_TM6 %>%
ggplot(aes(x=type,y=Degradation,color=type,fill=type))+
geom_jitter(width = 0.2, size = 1.5, show.legend = FALSE)+
geom_boxplot(alpha=0.2,outlier.shape = NA, width = 0.5, show.legend = FALSE, coef=0)+
stat_compare_means() +
theme(axis.text.x = element_text(vjust = 0.5, size=10),
axis.text.y = element_text(size=10),
axis.title=element_text(size=12,face="bold"),
axis.text = element_text(face="bold", size=18),
panel.background = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(size = 0.5, linetype = "solid", colour = "black"),
legend.text = element_text(size=10),
legend.title = element_text(size=12),
legend.position="none",
legend.key.size = unit(1, 'cm'),
strip.text.x = element_text(size = 12, color = "black", face = "bold"))+
labs( x = "type")
#Structure
p3 <-merge_gift_TM6 %>%
ggplot(aes(x=type,y=Structure,color=type,fill=type))+
geom_jitter(width = 0.2, size = 1.5, show.legend = FALSE)+
geom_boxplot(alpha=0.2,outlier.shape = NA, width = 0.5, show.legend = FALSE, coef=0)+
stat_compare_means() +
theme(axis.text.x = element_text(vjust = 3, size=10),
axis.text.y = element_text(size=10),
axis.title=element_text(size=12,face="bold"),
axis.text = element_text(face="bold", size=18),
panel.background = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(size = 0.5, linetype = "solid", colour = "black"),
legend.text = element_text(size=10),
legend.title = element_text(size=12),
legend.position="none",
legend.key.size = unit(1, 'cm'),
strip.text.x = element_text(size = 12, color = "black", face = "bold"))+
labs( x = "type")
7.9.4.2 CI vs WC
#Merge the functional domains with the metadata
merge_gift_TM8 <- GIFTs_domains_community %>%
as.data.frame() %>%
rownames_to_column(., "Tube_code") %>%
inner_join(., sample_metadata_TM8, by="Tube_code")#Biosynthesis
p1 <-merge_gift_TM8 %>%
ggplot(aes(x=type,y=Biosynthesis,color=type,fill=type))+
geom_jitter(width = 0.2, size = 1.5, show.legend = FALSE)+
geom_boxplot(alpha=0.2,outlier.shape = NA, width = 0.5, show.legend = FALSE, coef=0)+
stat_compare_means() +
theme(axis.text.x = element_text(vjust = 0.5, size=10),
axis.text.y = element_text(size=10),
axis.title=element_text(size=12,face="bold"),
axis.text = element_text(face="bold", size=18),
panel.background = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(size = 0.5, linetype = "solid", colour = "black"),
legend.text = element_text(size=10),
legend.title = element_text(size=12),
legend.position="none",
legend.key.size = unit(1, 'cm'),
strip.text.x = element_text(size = 12, color = "black", face = "bold"))+
labs( x = "Type")
#Degradation
p2 <-merge_gift_TM8 %>%
ggplot(aes(x=type,y=Degradation,color=type,fill=type))+
geom_jitter(width = 0.2, size = 1.5, show.legend = FALSE)+
geom_boxplot(alpha=0.2,outlier.shape = NA, width = 0.5, show.legend = FALSE, coef=0)+
stat_compare_means() +
theme(axis.text.x = element_text(vjust = 0.5, size=10),
axis.text.y = element_text(size=10),
axis.title=element_text(size=12,face="bold"),
axis.text = element_text(face="bold", size=18),
panel.background = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(size = 0.5, linetype = "solid", colour = "black"),
legend.text = element_text(size=10),
legend.title = element_text(size=12),
legend.position="none",
legend.key.size = unit(1, 'cm'),
strip.text.x = element_text(size = 12, color = "black", face = "bold"))+
labs( x = "type")
#Structure
p3 <-merge_gift_TM8 %>%
ggplot(aes(x=type,y=Structure,color=type,fill=type))+
geom_jitter(width = 0.2, size = 1.5, show.legend = FALSE)+
geom_boxplot(alpha=0.2,outlier.shape = NA, width = 0.5, show.legend = FALSE, coef=0)+
stat_compare_means() +
theme(axis.text.x = element_text(vjust = 3, size=10),
axis.text.y = element_text(size=10),
axis.title=element_text(size=12,face="bold"),
axis.text = element_text(face="bold", size=18),
panel.background = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(size = 0.5, linetype = "solid", colour = "black"),
legend.text = element_text(size=10),
legend.title = element_text(size=12),
legend.position="none",
legend.key.size = unit(1, 'cm'),
strip.text.x = element_text(size = 12, color = "black", face = "bold"))+
labs( x = "type")